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Next-generation Sequencing

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Related Experiment Video

Updated: Nov 7, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Whole genome sequencing in oncology: using scenario drafting to explore future developments.

Michiel van de Ven¹, Martijn J H G Simons^2,3, Hendrik Koffijberg¹

¹Technical Medical Centre, University of Twente, Enschede, The Netherlands.

|May 2, 2021

Summary

Whole Genome Sequencing (WGS) in oncology is hindered by cost and utility concerns. Future research should focus on reducing price, improving turnaround time, and enhancing clinical utility for wider adoption.

Keywords:

Implementation Oncology Scenario drafting Uncertainty Whole genome sequencing

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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

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Published on: September 20, 2016

Area of Science:

Genomics
Oncology
Molecular Diagnostics

Background:

Whole Genome Sequencing (WGS) implementation in oncology faces challenges including infrastructure, cost, and unclear clinical utility.
Uncertainties impede widespread adoption of WGS as a standard molecular diagnostic tool in cancer care.

Purpose of the Study:

To investigate future developments influencing the adoption of WGS in oncology through scenario drafting.
To identify key facilitators and barriers for WGS implementation in clinical cancer diagnostics.

Main Methods:

A four-step scenario drafting process involving literature review, expert prioritization, scenario development, and online expert surveys.
Program Evaluation and Review Technique (PERT) and unweighted linear pooling were used to analyze expert opinions on scenario likelihood.

Main Results:

Price, clinical utility, and turnaround time were identified as the most critical factors for WGS implementation.
The most likely scenario (68.3% median likelihood) involves WGS as a clinical diagnostic with reduced cost, faster turnaround, and improved actionability.
Scenarios regarding enhanced treatment response and centralized WGS showed lower likelihoods.

Conclusions:

WGS implementation in oncology hinges on cost, clinical utility (actionable targets and treatment value), and turnaround time.
Further research should prioritize these factors and explore optimal service delivery models for WGS.
Strategic decision-making for WGS adoption requires comprehensive data and stakeholder support.